Automated Breast Boundary Segmentation to Improve the Accuracy of Identifying Abnormalities in Breast Thermograms

References

• WHO, Cancer. Available: https://www.who.int/en/news-room/fact-sheets/detail/cancer, 2019.
   Google Scholar
• L. Neumann, R. M. Nowak, R. Okuniewski, W. Oleszkiewicz, P. Cichosz, D. Jagodziński, and M. Matysiewicz, “Preprocessing for classification of thermograms in breast cancer detection," in Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments, Vol. 10031, 2016, pp. 903–910. SPIE.
   Google Scholar
• D. A. Kennedy, T. Lee, and D. Seely, “A comparative review of thermography as a breast cancer screening technique,” Integr. Cancer Ther., Vol. 8, no. 1, pp. 9–16, 2009.
   PubMed Web of Science ®Google Scholar
• S. S. Suganthi, and S. Ramakrishnan, “Anisotropic diffusion filter-based edge enhancement for segmentation of breast thermogram using level sets,” Biomed. Signal. Process. Control., Vol. 10, pp. 128–36, 2014.
   Web of Science ®Google Scholar
• N. Venkatachalam, L. Shanmugam, G. C. Heltin, G. Govindarajan, and P. Sasipriya, “Enhanced segmentation of inflamed ROI to improve the accuracy of identifying benign and malignant cases in breast thermogram,” J. Oncol., Vol. 2021, pp. 1–17, 2021.
   Web of Science ®Google Scholar
• R. Rastghalam, and H. Pourghassem, “Breast cancer detection using MRF-based probable texture feature and decision-level fusion-based classification using HMM on thermography images,” Pattern Recognit., Vol. 51, pp. 176–86, 2016.
   Web of Science ®Google Scholar
• T. B. Borchartt, R. Resmini, and A. Conci, “Thermal feature analysis to aid on breast disease diagnosis,” in 21st Brazilian Congress of Mechanical Engineering Natal RN, Brazil. Proceedings of COBEM, ABCM, 2011, pp. 24–28.
   Google Scholar
• E. Mahmoudzadeh, M. Zekri, M. A. Montazeri, S. Sadri, and S. T. Dabbagh, “Directional SUSAN image boundary detection of breast thermogram,” IET Image Proc., Vol. 10, no. 7, pp. 552–60, 2016.
   Web of Science ®Google Scholar
• H. Qi, and J. F. Head, “Asymmetry analysis using automatic segmentation and classification for breast cancer detection inthermograms,” in Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Istanbul, Turkey, 2001, pp. 2866–9.
   Google Scholar
• D. Sathish, S. Kamath, K. Prasad, R. Kadavigere, and R. J. Martis, “Asymmetry analysis of breast thermograms using automated segmentation and texture features,” Signal Image Video Process, Vol. 11, no. 4, pp. 745–52, 2017.
   Web of Science ®Google Scholar
• A. Hossam, H. H. Mand, and H. M. Abd El Kader, “Automatic image segmentation method for breast cancer analysis using thermography,” J. Eng. Sci., Vol. 46, no. 1, pp. 12–32, 2018.
   Google Scholar
• M. A. Chen Bao-ping, and M. A. Zeng-qiang, “Automated image segmentation and asymmetry analysis for breast using infrared images,” in International Workshop on Education Technology and Training & International Workshop on Geoscience and Remote Sensing, IEEE, Vol. 1, pp. 410–3, 2008.
   Google Scholar
• S. Pramanik, S. Ghosh, D. Bhattacharjee, and M. Nasipuri, “Segmentation of breast-region in breast thermogram using arc-approximation and triangular-space search,” IEEE Trans. Instrum. Meas., Vol. 69, no. 7, pp. 4785–95, 2019.
   Web of Science ®Google Scholar
• H. Qi, W. E. Snyder, J. F. Head, and R. L. Elliott, “Detecting breast cancer from infrared images by asymmetry analysis,” in Engineering in Medicine and Biology Society Proceedings of the 22nd Annual International Conference of the IEEE, Chicago, Vol. 2, 2000, pp. 1227–8.
   Google Scholar
• M. Moghbel, S. Mashohor, R. Mahmud, M. Saripan, S. A. Hamid, S. M. Sani, and S. Nizam, “Breast boundary segmentation in thermography images based on random walkers,” Turk. J. Electr. Eng. Comput. Sci., Vol. 25, no. 3, pp. 1733–50, 2017.
   Web of Science ®Google Scholar
• S. Tello-Mijares, F. Woo, and F. Flores, “Breast cancer identification via thermography image segmentation with a gradient vector flow and a convolutional neural network,” J. Healthc. Eng., Vol. 2019, pp. 1–13, 2019.
   Google Scholar
• R. Ramya Devi, and G. S. Anandhamala, “Analysis of breast thermograms using asymmetry in infra-mammary curves,” J. Med. Syst., Vol. 43, pp. 1–9, 2019.
   Web of Science ®Google Scholar
• D. Sánchez-Ruiz, I. Olmos-Pineda, and J. Arturo Olvera-López, “Automatic region of interest segmentation for breast thermogram image classification,” Pattern Recognit. Lett., Vol. 135, pp. 72–81, 2020.
   Web of Science ®Google Scholar
• R. M. Haralick, “Statistical and structural approaches to texture,” Proc. IEEE, Vol. 67, no. 5, pp. 786–804, 1979.
   Web of Science ®Google Scholar
• L. F. Silva, D. C. M. Saade, G. O. Sequeiros, A. C. Silva, A. C. Paiva, R. S. Bravo, and A. Conci, “A new database for breast research with infrared image,” J. Med. Imaging. Health. Inform., Vol. 4, no. 1, pp. 92–100, 2014.
   Web of Science ®Google Scholar
• S. Leninisha, and K. Vani, “Water flow based geometric active deformable model for road network,” ISPRS. J. Photogramm. Remote. Sens., Vol. 102, pp. 140–7, 2015.
   Web of Science ®Google Scholar
• R. Cárdenes, R. de Luis-García, and M. Bach-Cuadra, “A multidimensional segmentation evaluation for medical image data,” Comput. Methods Programs Biomed., Vol. 96, no. 2, pp. 108–124, 2009.
   PubMed Web of Science ®Google Scholar
• S. Leninisha, and K. Vani, “A junction aware water flow approach for urban road network extraction,” J. IET Image Process., Vol. 11, pp. 227–34, 2016.
   Google Scholar
• L. Shanmugam, K. Gunasekaran, A. Natarajan, and V. Kaliaperumal, “Quantitative growth analysis of pulp necrotic tooth (post-op) using modified region growing active contour model,” IET Image Proc., Vol. 11, pp. 1015–9, 2017.
   Web of Science ®Google Scholar